Results 1 to 10 of about 617,011 (320)
Robust Confidence Intervals for PM2.5 Concentration Measurements in the Ecuadorian Park La Carolina [PDF]
Sensors, 2020 In this article, robust confidence intervals for PM2.5 (particles with size less than or equal to 2.5 μ m ) concentration measurements performed in La Carolina Park, Quito, Ecuador, have been built.
Wilmar Hernandez +3 more
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Bootstrapping Confidence Intervals For Robust Measures Of Association [PDF]
Journal of Modern Applied Statistical Methods, 2003 A Monte Carlo simulation study compared four bootstrapping procedures in generating confidence intervals for the robust Winsorized and percentage bend correlations. Results revealed the superior resiliency of the robust correlations over r, with neither outperforming the other.
Jason E. King
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Dependence-Robust Confidence Intervals for Capture-Recapture Surveys. [PDF]
J Surv Stat Methodol, 2023 Abstract Capture–recapture (CRC) surveys are used to estimate the size of a population whose members cannot be enumerated directly. CRC surveys have been used to estimate the number of Coronavirus Disease 2019 (COVID-19) infections, people who use drugs, sex workers, conflict casualties, and trafficking victims.
Sun J +3 more
europepmc +6 more sources
Robust Method for Confidence Interval Estimation in Outlier-Prone Datasets: Application to Molecular and Biophysical Data [PDF]
BiomoleculesEstimating confidence intervals in small or noisy datasets is a recurring challenge in biomolecular research, particularly when data contain outliers or exhibit high variability.
Victor V. Golovko
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Bootstrap Confidence Intervals for 11 Robust Correlations in the Presence of Outliers and Leverage Observations [PDF]
Methodology, 2022 Researchers often examine whether two continuous variables (X and Y) are linearly related. Pearson’s correlation (r) is a widely-employed statistic for assessing bivariate linearity.
Johnson Ching-Hong Li
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New robust confidence intervals for the mean under dependence [PDF]
Journal of Statistical Planning and Inference, 2020 The goal of this paper is to indicate a new method for constructing normal confidence intervals for the mean, when the data is coming from stochastic structures with possibly long memory, especially when the dependence structure is not known or even the existence of the density function.
Martial Longla, Magda Peligrad
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On the binomial confidence interval and probabilistic robust control [PDF]
Automatica, 2004 6 pages, 1 ...
Xinjia Chen, Kemin Zhou, J.L. Aravena
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Globally Robust Confidence Intervals for Location
Dhaka University Journal of Science, 2012 Classical inference considers sampling variability to be the only source of uncertainty, and does not address the issue of bias caused by contamination. Naive robust intervals replace the classical estimates by their robust counterparts without considering the possible bias of the robust point estimates. Consequently, the asymptotic coverage proportion
M. Ershadul Haque, Jafar A Khan
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Robust Empirical Bayes Confidence Intervals
Econometrica, 2020 We construct robust empirical Bayes confidence intervals (EBCIs) in a normal means problem. The intervals are centered at the usual linear empirical Bayes estimator, but use a critical value accounting for shrinkage. Parametric EBCIs that assume a normal distribution for the means (Morris (1983b)) may substantially undercover when this assumption is ...
Timothy B. Armstrong +2 more
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Confidence intervals for robust estimates of measurement uncertainty [PDF]
Accreditation and Quality Assurance, 2020 AbstractUncertainties arising at different stages of a measurement process can be estimated using analysis of variance (ANOVA) on duplicated measurements. In some cases, it is also desirable to calculate confidence intervals for these uncertainties. This can be achieved using probability models that assume the measurement data are normally distributed.
Peter D. Rostron +2 more
openalex +5 more sources